Overview

Dataset statistics

Number of variables32
Number of observations40000
Missing cells44194
Missing cells (%)3.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.8 MiB
Average record size in memory256.0 B

Variable types

Numeric10
Categorical11
Unsupported1
Boolean10

Alerts

QUANT_DEPENDANTS has constant value "0" Constant
FLAG_OTHER_CARD has constant value "False" Constant
QUANT_BANKING_ACCOUNTS has constant value "0" Constant
FLAG_MOBILE_PHONE has constant value "False" Constant
FLAG_CONTACT_PHONE has constant value "False" Constant
COD_APPLICATION_BOOTH has constant value "0" Constant
FLAG_CARD_INSURANCE_OPTION has constant value "False" Constant
PERSONAL_REFERENCE_#1 has a high cardinality: 17572 distinct values High cardinality
PERSONAL_REFERENCE_#2 has a high cardinality: 13672 distinct values High cardinality
QUANT_ADDITIONAL_CARDS_IN_THE_APPLICATION is highly correlated with FLAG_MOBILE_PHONE and 6 other fieldsHigh correlation
FLAG_MOBILE_PHONE is highly correlated with QUANT_ADDITIONAL_CARDS_IN_THE_APPLICATION and 17 other fieldsHigh correlation
FLAG_RESIDENCIAL_PHONE is highly correlated with FLAG_MOBILE_PHONE and 6 other fieldsHigh correlation
RESIDENCE_TYPE is highly correlated with FLAG_MOBILE_PHONE and 6 other fieldsHigh correlation
FLAG_MOTHERS_NAME is highly correlated with FLAG_MOBILE_PHONE and 6 other fieldsHigh correlation
FLAG_CARD_INSURANCE_OPTION is highly correlated with QUANT_ADDITIONAL_CARDS_IN_THE_APPLICATION and 17 other fieldsHigh correlation
FLAG_CONTACT_PHONE is highly correlated with QUANT_ADDITIONAL_CARDS_IN_THE_APPLICATION and 17 other fieldsHigh correlation
QUANT_BANKING_ACCOUNTS is highly correlated with QUANT_ADDITIONAL_CARDS_IN_THE_APPLICATION and 17 other fieldsHigh correlation
MARITAL_STATUS is highly correlated with FLAG_MOBILE_PHONE and 6 other fieldsHigh correlation
COD_APPLICATION_BOOTH is highly correlated with QUANT_ADDITIONAL_CARDS_IN_THE_APPLICATION and 17 other fieldsHigh correlation
FLAG_FATHERS_NAME is highly correlated with FLAG_MOBILE_PHONE and 6 other fieldsHigh correlation
SEX is highly correlated with FLAG_MOBILE_PHONE and 6 other fieldsHigh correlation
FLAG_OTHER_CARD is highly correlated with QUANT_ADDITIONAL_CARDS_IN_THE_APPLICATION and 17 other fieldsHigh correlation
FLAG_RESIDENCE_STATE=WORKING_STATE is highly correlated with FLAG_MOBILE_PHONE and 6 other fieldsHigh correlation
FLAG_RESIDENCIAL_ADDRESS=POSTAL_ADDRESS is highly correlated with FLAG_MOBILE_PHONE and 6 other fieldsHigh correlation
SHOP_RANK is highly correlated with FLAG_MOBILE_PHONE and 6 other fieldsHigh correlation
TARGET_LABEL_BAD=1 is highly correlated with FLAG_MOBILE_PHONE and 6 other fieldsHigh correlation
QUANT_DEPENDANTS is highly correlated with QUANT_ADDITIONAL_CARDS_IN_THE_APPLICATION and 17 other fieldsHigh correlation
FLAG_RESIDENCE_TOWN=WORKING_TOWN is highly correlated with FLAG_MOBILE_PHONE and 6 other fieldsHigh correlation
MARITAL_STATUS is highly correlated with AGEHigh correlation
AGE is highly correlated with MARITAL_STATUSHigh correlation
FLAG_RESIDENCIAL_PHONE is highly correlated with AREA_CODE_RESIDENCIAL_PHONEHigh correlation
AREA_CODE_RESIDENCIAL_PHONE is highly correlated with FLAG_RESIDENCIAL_PHONEHigh correlation
EDUCATION has 40000 (100.0%) missing values Missing
PERSONAL_REFERENCE_#2 has 4190 (10.5%) missing values Missing
MATE_INCOME is highly skewed (γ1 = 73.04313799) Skewed
PERSONAL_NET_INCOME is highly skewed (γ1 = 55.52751098) Skewed
ID_CLIENT is uniformly distributed Uniform
ID_CLIENT has unique values Unique
EDUCATION is an unsupported type, check if it needs cleaning or further analysis Unsupported
MONTHS_IN_RESIDENCE has 774 (1.9%) zeros Zeros
MONTHS_IN_THE_JOB has 8347 (20.9%) zeros Zeros
MATE_INCOME has 38411 (96.0%) zeros Zeros
PERSONAL_NET_INCOME has 956 (2.4%) zeros Zeros

Reproduction

Analysis started2022-06-22 21:46:09.116427
Analysis finished2022-06-22 21:51:11.991337
Duration5 minutes and 2.87 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

ID_CLIENT
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct40000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24982.227
Minimum2
Maximum50000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size312.6 KiB
2022-06-22T16:51:13.092337image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2475.9
Q112458.75
median25058.5
Q337425.25
95-th percentile47518.1
Maximum50000
Range49998
Interquartile range (IQR)24966.5

Descriptive statistics

Standard deviation14428.53176
Coefficient of variation (CV)0.5775518635
Kurtosis-1.196545227
Mean24982.227
Median Absolute Deviation (MAD)12483
Skewness-0.0005505929594
Sum999289080
Variance208182528.7
MonotonicityStrictly increasing
2022-06-22T16:51:14.250357image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21
 
< 0.1%
332751
 
< 0.1%
332671
 
< 0.1%
332691
 
< 0.1%
332701
 
< 0.1%
332711
 
< 0.1%
332721
 
< 0.1%
332731
 
< 0.1%
332741
 
< 0.1%
332761
 
< 0.1%
Other values (39990)39990
> 99.9%
ValueCountFrequency (%)
21
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
101
< 0.1%
111
< 0.1%
121
< 0.1%
ValueCountFrequency (%)
500001
< 0.1%
499981
< 0.1%
499961
< 0.1%
499951
< 0.1%
499941
< 0.1%
499931
< 0.1%
499921
< 0.1%
499911
< 0.1%
499901
< 0.1%
499891
< 0.1%

ID_SHOP
Real number (ℝ≥0)

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.82295
Minimum1
Maximum96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size312.6 KiB
2022-06-22T16:51:15.314363image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q112
median21
Q324
95-th percentile55
Maximum96
Range95
Interquartile range (IQR)12

Descriptive statistics

Standard deviation14.57191346
Coefficient of variation (CV)0.6998006266
Kurtosis4.02119351
Mean20.82295
Median Absolute Deviation (MAD)4
Skewness1.683848981
Sum832918
Variance212.3406618
MonotonicityNot monotonic
2022-06-22T16:51:16.255337image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
255356
 
13.4%
223914
 
9.8%
243502
 
8.8%
553409
 
8.5%
232375
 
5.9%
201811
 
4.5%
11616
 
4.0%
121551
 
3.9%
151542
 
3.9%
191384
 
3.5%
Other values (21)13540
33.9%
ValueCountFrequency (%)
11616
4.0%
2616
 
1.5%
31356
3.4%
4540
 
1.4%
5503
 
1.3%
6577
 
1.4%
7680
1.7%
8489
 
1.2%
9986
2.5%
101294
3.2%
ValueCountFrequency (%)
96179
 
0.4%
815
 
< 0.1%
771
 
< 0.1%
66371
 
0.9%
553409
8.5%
505
 
< 0.1%
255356
13.4%
243502
8.8%
232375
5.9%
223914
9.8%

SEX
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing3
Missing (%)< 0.1%
Memory size312.6 KiB
F
27903 
M
12094 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters39997
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowF
3rd rowF
4th rowM
5th rowF

Common Values

ValueCountFrequency (%)
F27903
69.8%
M12094
30.2%
(Missing)3
 
< 0.1%

Length

2022-06-22T16:51:17.121360image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-22T16:51:18.236335image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
f27903
69.8%
m12094
30.2%

Most occurring characters

ValueCountFrequency (%)
F27903
69.8%
M12094
30.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter39997
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F27903
69.8%
M12094
30.2%

Most occurring scripts

ValueCountFrequency (%)
Latin39997
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
F27903
69.8%
M12094
30.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII39997
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F27903
69.8%
M12094
30.2%

MARITAL_STATUS
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.6 KiB
S
20375 
C
13721 
O
2220 
V
 
1961
D
 
1723

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters40000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS
2nd rowC
3rd rowS
4th rowS
5th rowS

Common Values

ValueCountFrequency (%)
S20375
50.9%
C13721
34.3%
O2220
 
5.5%
V1961
 
4.9%
D1723
 
4.3%

Length

2022-06-22T16:51:18.834340image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-22T16:51:19.463336image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
s20375
50.9%
c13721
34.3%
o2220
 
5.5%
v1961
 
4.9%
d1723
 
4.3%

Most occurring characters

ValueCountFrequency (%)
S20375
50.9%
C13721
34.3%
O2220
 
5.5%
V1961
 
4.9%
D1723
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter40000
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S20375
50.9%
C13721
34.3%
O2220
 
5.5%
V1961
 
4.9%
D1723
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
Latin40000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S20375
50.9%
C13721
34.3%
O2220
 
5.5%
V1961
 
4.9%
D1723
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII40000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S20375
50.9%
C13721
34.3%
O2220
 
5.5%
V1961
 
4.9%
D1723
 
4.3%

AGE
Real number (ℝ≥0)

HIGH CORRELATION

Distinct72
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.649725
Minimum15
Maximum88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size312.6 KiB
2022-06-22T16:51:20.058346image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile19
Q123
median33
Q343
95-th percentile60
Maximum88
Range73
Interquartile range (IQR)20

Descriptive statistics

Standard deviation13.07620003
Coefficient of variation (CV)0.3773825052
Kurtosis-0.01990747593
Mean34.649725
Median Absolute Deviation (MAD)10
Skewness0.7616857594
Sum1385989
Variance170.9870071
MonotonicityNot monotonic
2022-06-22T16:51:20.911364image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
202022
 
5.1%
192007
 
5.0%
211808
 
4.5%
221567
 
3.9%
181486
 
3.7%
231351
 
3.4%
241244
 
3.1%
251170
 
2.9%
281117
 
2.8%
261073
 
2.7%
Other values (62)25155
62.9%
ValueCountFrequency (%)
155
 
< 0.1%
1612
 
< 0.1%
1772
 
0.2%
181486
3.7%
192007
5.0%
202022
5.1%
211808
4.5%
221567
3.9%
231351
3.4%
241244
3.1%
ValueCountFrequency (%)
882
 
< 0.1%
861
 
< 0.1%
841
 
< 0.1%
836
< 0.1%
826
< 0.1%
817
< 0.1%
807
< 0.1%
7910
< 0.1%
7811
< 0.1%
7713
< 0.1%

QUANT_DEPENDANTS
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.6 KiB
0
40000 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters40000
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
040000
100.0%

Length

2022-06-22T16:51:21.983361image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-22T16:51:22.821689image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
040000
100.0%

Most occurring characters

ValueCountFrequency (%)
040000
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number40000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
040000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common40000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
040000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII40000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
040000
100.0%

EDUCATION
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing40000
Missing (%)100.0%
Memory size312.6 KiB

FLAG_RESIDENCIAL_PHONE
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
True
32649 
False
7351 
ValueCountFrequency (%)
True32649
81.6%
False7351
 
18.4%
2022-06-22T16:51:23.575714image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

AREA_CODE_RESIDENCIAL_PHONE
Real number (ℝ≥0)

HIGH CORRELATION

Distinct59
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.812275
Minimum1
Maximum70
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size312.6 KiB
2022-06-22T16:51:24.448690image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile23
Q131
median31
Q331
95-th percentile50
Maximum70
Range69
Interquartile range (IQR)0

Descriptive statistics

Standard deviation10.4029414
Coefficient of variation (CV)0.3076675972
Kurtosis1.285351655
Mean33.812275
Median Absolute Deviation (MAD)0
Skewness-0.2287684343
Sum1352491
Variance108.2211899
MonotonicityNot monotonic
2022-06-22T16:51:25.414691image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3128080
70.2%
508884
 
22.2%
51951
 
4.9%
23793
 
2.0%
2490
 
0.2%
4937
 
0.1%
3226
 
0.1%
2720
 
0.1%
4212
 
< 0.1%
3810
 
< 0.1%
Other values (49)97
 
0.2%
ValueCountFrequency (%)
12
 
< 0.1%
23
 
< 0.1%
31
 
< 0.1%
41
 
< 0.1%
51951
4.9%
62
 
< 0.1%
72
 
< 0.1%
83
 
< 0.1%
91
 
< 0.1%
101
 
< 0.1%
ValueCountFrequency (%)
701
 
< 0.1%
692
 
< 0.1%
686
< 0.1%
672
 
< 0.1%
651
 
< 0.1%
641
 
< 0.1%
631
 
< 0.1%
621
 
< 0.1%
601
 
< 0.1%
591
 
< 0.1%

PAYMENT_DAY
Real number (ℝ≥0)

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.31395
Minimum1
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size312.6 KiB
2022-06-22T16:51:26.869716image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q19
median12
Q320
95-th percentile28
Maximum28
Range27
Interquartile range (IQR)11

Descriptive statistics

Standard deviation7.159756766
Coefficient of variation (CV)0.4675316797
Kurtosis-0.8118152063
Mean15.31395
Median Absolute Deviation (MAD)6
Skewness0.1133339367
Sum612558
Variance51.26211695
MonotonicityNot monotonic
2022-06-22T16:51:27.646716image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1210150
25.4%
87206
18.0%
186638
16.6%
205050
12.6%
283604
 
9.0%
252705
 
6.8%
31563
 
3.9%
231157
 
2.9%
11091
 
2.7%
16213
 
0.5%
Other values (6)623
 
1.6%
ValueCountFrequency (%)
11091
 
2.7%
31563
 
3.9%
6124
 
0.3%
87206
18.0%
9104
 
0.3%
11213
 
0.5%
1210150
25.4%
151
 
< 0.1%
16213
 
0.5%
186638
16.6%
ValueCountFrequency (%)
283604
 
9.0%
2754
 
0.1%
252705
 
6.8%
231157
 
2.9%
22127
 
0.3%
205050
12.6%
186638
16.6%
16213
 
0.5%
151
 
< 0.1%
1210150
25.4%

SHOP_RANK
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.6 KiB
0
39770 
3
 
178
2
 
52

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters40000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
039770
99.4%
3178
 
0.4%
252
 
0.1%

Length

2022-06-22T16:51:28.515692image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-22T16:51:29.380716image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
039770
99.4%
3178
 
0.4%
252
 
0.1%

Most occurring characters

ValueCountFrequency (%)
039770
99.4%
3178
 
0.4%
252
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number40000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
039770
99.4%
3178
 
0.4%
252
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common40000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
039770
99.4%
3178
 
0.4%
252
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII40000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
039770
99.4%
3178
 
0.4%
252
 
0.1%

RESIDENCE_TYPE
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.6 KiB
P
29752 
A
5130 
C
3471 
O
 
1647

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters40000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowP
2nd rowP
3rd rowO
4th rowP
5th rowA

Common Values

ValueCountFrequency (%)
P29752
74.4%
A5130
 
12.8%
C3471
 
8.7%
O1647
 
4.1%

Length

2022-06-22T16:51:30.080692image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-22T16:51:30.872689image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
p29752
74.4%
a5130
 
12.8%
c3471
 
8.7%
o1647
 
4.1%

Most occurring characters

ValueCountFrequency (%)
P29752
74.4%
A5130
 
12.8%
C3471
 
8.7%
O1647
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter40000
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P29752
74.4%
A5130
 
12.8%
C3471
 
8.7%
O1647
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
Latin40000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
P29752
74.4%
A5130
 
12.8%
C3471
 
8.7%
O1647
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII40000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P29752
74.4%
A5130
 
12.8%
C3471
 
8.7%
O1647
 
4.1%

MONTHS_IN_RESIDENCE
Real number (ℝ≥0)

ZEROS

Distinct76
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean152.9211
Minimum0
Maximum1188
Zeros774
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size312.6 KiB
2022-06-22T16:51:31.815693image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12
Q136
median120
Q3240
95-th percentile420
Maximum1188
Range1188
Interquartile range (IQR)204

Descriptive statistics

Standard deviation136.0969619
Coefficient of variation (CV)0.889981578
Kurtosis1.081098854
Mean152.9211
Median Absolute Deviation (MAD)96
Skewness1.092675097
Sum6116844
Variance18522.38303
MonotonicityNot monotonic
2022-06-22T16:51:33.074700image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
124933
 
12.3%
242902
 
7.3%
1202794
 
7.0%
2402768
 
6.9%
362419
 
6.0%
601966
 
4.9%
481758
 
4.4%
721502
 
3.8%
1801363
 
3.4%
3601316
 
3.3%
Other values (66)16279
40.7%
ValueCountFrequency (%)
0774
 
1.9%
124933
12.3%
242902
7.3%
362419
6.0%
481758
 
4.4%
601966
 
4.9%
721502
 
3.8%
84990
 
2.5%
961222
 
3.1%
108664
 
1.7%
ValueCountFrequency (%)
11881
 
< 0.1%
11761
 
< 0.1%
11161
 
< 0.1%
10201
 
< 0.1%
9001
 
< 0.1%
8882
< 0.1%
8521
 
< 0.1%
8403
< 0.1%
8282
< 0.1%
8162
< 0.1%

FLAG_MOTHERS_NAME
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
True
39850 
False
 
150
ValueCountFrequency (%)
True39850
99.6%
False150
 
0.4%
2022-06-22T16:51:34.061710image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

FLAG_FATHERS_NAME
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
True
38342 
False
 
1658
ValueCountFrequency (%)
True38342
95.9%
False1658
 
4.1%
2022-06-22T16:51:34.836736image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

FLAG_RESIDENCE_TOWN=WORKING_TOWN
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
False
21740 
True
18260 
ValueCountFrequency (%)
False21740
54.4%
True18260
45.6%
2022-06-22T16:51:35.670823image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

FLAG_RESIDENCE_STATE=WORKING_STATE
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
True
39653 
False
 
347
ValueCountFrequency (%)
True39653
99.1%
False347
 
0.9%
2022-06-22T16:51:36.497821image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

MONTHS_IN_THE_JOB
Real number (ℝ≥0)

ZEROS

Distinct54
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.6295
Minimum0
Maximum1176
Zeros8347
Zeros (%)20.9%
Negative0
Negative (%)0.0%
Memory size312.6 KiB
2022-06-22T16:51:37.224095image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q112
median24
Q360
95-th percentile228
Maximum1176
Range1176
Interquartile range (IQR)48

Descriptive statistics

Standard deviation73.87513904
Coefficient of variation (CV)1.459132305
Kurtosis8.923171323
Mean50.6295
Median Absolute Deviation (MAD)24
Skewness2.527016428
Sum2025180
Variance5457.536168
MonotonicityNot monotonic
2022-06-22T16:51:37.944703image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1211593
29.0%
08347
20.9%
244018
 
10.0%
362899
 
7.2%
601932
 
4.8%
481893
 
4.7%
721393
 
3.5%
1201249
 
3.1%
96777
 
1.9%
84776
 
1.9%
Other values (44)5123
12.8%
ValueCountFrequency (%)
08347
20.9%
1211593
29.0%
244018
 
10.0%
362899
 
7.2%
481893
 
4.7%
601932
 
4.8%
721393
 
3.5%
84776
 
1.9%
96777
 
1.9%
108405
 
1.0%
ValueCountFrequency (%)
11761
 
< 0.1%
11041
 
< 0.1%
7801
 
< 0.1%
7081
 
< 0.1%
6842
< 0.1%
6601
 
< 0.1%
6121
 
< 0.1%
6003
< 0.1%
5881
 
< 0.1%
5521
 
< 0.1%

PROFESSION_CODE
Real number (ℝ≥0)

Distinct291
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean484.611875
Minimum0
Maximum999
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size312.6 KiB
2022-06-22T16:51:39.073989image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13
Q188
median514
Q3865
95-th percentile999
Maximum999
Range999
Interquartile range (IQR)777

Descriptive statistics

Standard deviation382.1023719
Coefficient of variation (CV)0.7884709221
Kurtosis-1.661763842
Mean484.611875
Median Absolute Deviation (MAD)408
Skewness0.07352655483
Sum19384475
Variance146002.2226
MonotonicityNot monotonic
2022-06-22T16:51:40.394915image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9995084
 
12.7%
9503736
 
9.3%
132002
 
5.0%
2051786
 
4.5%
7031547
 
3.9%
261518
 
3.8%
1311046
 
2.6%
514996
 
2.5%
60959
 
2.4%
40774
 
1.9%
Other values (281)20552
51.4%
ValueCountFrequency (%)
02
 
< 0.1%
148
 
0.1%
2110
0.3%
35
 
< 0.1%
4209
0.5%
52
 
< 0.1%
61
 
< 0.1%
710
 
< 0.1%
85
 
< 0.1%
91
 
< 0.1%
ValueCountFrequency (%)
9995084
12.7%
992238
 
0.6%
99163
 
0.2%
9901
 
< 0.1%
95418
 
< 0.1%
953223
 
0.6%
952155
 
0.4%
9516
 
< 0.1%
9503736
9.3%
92427
 
0.1%

MATE_INCOME
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct561
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.99333325
Minimum0
Maximum70000
Zeros38411
Zeros (%)96.0%
Negative0
Negative (%)0.0%
Memory size312.6 KiB
2022-06-22T16:51:41.453398image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum70000
Range70000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation507.1591245
Coefficient of variation (CV)9.945596653
Kurtosis9259.924185
Mean50.99333325
Median Absolute Deviation (MAD)0
Skewness73.04313799
Sum2039733.33
Variance257210.3776
MonotonicityNot monotonic
2022-06-22T16:51:42.432716image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
038411
96.0%
100070
 
0.2%
50069
 
0.2%
60065
 
0.2%
80064
 
0.2%
150057
 
0.1%
200054
 
0.1%
120052
 
0.1%
40052
 
0.1%
70044
 
0.1%
Other values (551)1062
 
2.7%
ValueCountFrequency (%)
038411
96.0%
11
 
< 0.1%
1001
 
< 0.1%
1501
 
< 0.1%
1551
 
< 0.1%
18024
 
0.1%
1901
 
< 0.1%
1962
 
< 0.1%
20013
 
< 0.1%
2011
 
< 0.1%
ValueCountFrequency (%)
700001
 
< 0.1%
224001
 
< 0.1%
215401
 
< 0.1%
114761
 
< 0.1%
100002
< 0.1%
85131
 
< 0.1%
80002
< 0.1%
78001
 
< 0.1%
70003
< 0.1%
67721
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
True
39158 
False
 
842
ValueCountFrequency (%)
True39158
97.9%
False842
 
2.1%
2022-06-22T16:51:43.351597image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

FLAG_OTHER_CARD
Boolean

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
False
40000 
ValueCountFrequency (%)
False40000
100.0%
2022-06-22T16:51:44.361163image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

QUANT_BANKING_ACCOUNTS
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.6 KiB
0
40000 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters40000
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
040000
100.0%

Length

2022-06-22T16:51:44.854188image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-22T16:51:45.476101image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
040000
100.0%

Most occurring characters

ValueCountFrequency (%)
040000
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number40000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
040000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common40000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
040000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII40000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
040000
100.0%

PERSONAL_REFERENCE_#1
Categorical

HIGH CARDINALITY

Distinct17572
Distinct (%)43.9%
Missing1
Missing (%)< 0.1%
Memory size312.6 KiB
MARIA
 
552
MARCIA
 
269
FATIMA
 
254
SONIA
 
253
SANDRA
 
211
Other values (17567)
38460 

Length

Max length25
Median length21
Mean length10.03165079
Min length1

Characters and Unicode

Total characters401256
Distinct characters64
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15474 ?
Unique (%)38.7%

Sample

1st rowSARA
2nd rowJACI
3rd rowMARCIA CRISTINA ZANELLA
4th rowMARCIO
5th rowFABIO (NOIVO)

Common Values

ValueCountFrequency (%)
MARIA552
 
1.4%
MARCIA269
 
0.7%
FATIMA254
 
0.6%
SONIA253
 
0.6%
SANDRA211
 
0.5%
MARIA JOSE198
 
0.5%
LUCIA197
 
0.5%
CLAUDIA190
 
0.5%
VERA188
 
0.5%
REGINA182
 
0.5%
Other values (17562)37505
93.8%

Length

2022-06-22T16:51:46.086779image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
maria3338
 
5.1%
1265
 
1.9%
de997
 
1.5%
silva978
 
1.5%
da870
 
1.3%
ana787
 
1.2%
jose715
 
1.1%
lucia687
 
1.1%
santos484
 
0.7%
fatima479
 
0.7%
Other values (8843)54276
83.7%

Most occurring characters

ValueCountFrequency (%)
A70573
17.6%
I42535
10.6%
E37729
9.4%
R31109
 
7.8%
25651
 
6.4%
N25059
 
6.2%
L24333
 
6.1%
O22522
 
5.6%
S18852
 
4.7%
M14743
 
3.7%
Other values (54)88150
22.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter370879
92.4%
Space Separator25651
 
6.4%
Open Punctuation1247
 
0.3%
Close Punctuation1116
 
0.3%
Other Punctuation967
 
0.2%
Dash Punctuation864
 
0.2%
Decimal Number466
 
0.1%
Math Symbol59
 
< 0.1%
Modifier Symbol6
 
< 0.1%
Currency Symbol1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A70573
19.0%
I42535
11.5%
E37729
10.2%
R31109
8.4%
N25059
 
6.8%
L24333
 
6.6%
O22522
 
6.1%
S18852
 
5.1%
M14743
 
4.0%
D14630
 
3.9%
Other values (26)68794
18.5%
Decimal Number
ValueCountFrequency (%)
285
18.2%
158
12.4%
653
11.4%
048
10.3%
742
9.0%
538
8.2%
937
7.9%
436
7.7%
335
7.5%
834
 
7.3%
Other Punctuation
ValueCountFrequency (%)
/722
74.7%
*131
 
13.5%
.99
 
10.2%
,8
 
0.8%
\3
 
0.3%
'3
 
0.3%
&1
 
0.1%
Open Punctuation
ValueCountFrequency (%)
(1246
99.9%
[1
 
0.1%
Close Punctuation
ValueCountFrequency (%)
)1115
99.9%
]1
 
0.1%
Math Symbol
ValueCountFrequency (%)
=58
98.3%
>1
 
1.7%
Modifier Symbol
ValueCountFrequency (%)
`5
83.3%
´1
 
16.7%
Space Separator
ValueCountFrequency (%)
25651
100.0%
Dash Punctuation
ValueCountFrequency (%)
-864
100.0%
Currency Symbol
ValueCountFrequency (%)
$1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin370879
92.4%
Common30377
 
7.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
A70573
19.0%
I42535
11.5%
E37729
10.2%
R31109
8.4%
N25059
 
6.8%
L24333
 
6.6%
O22522
 
6.1%
S18852
 
5.1%
M14743
 
4.0%
D14630
 
3.9%
Other values (26)68794
18.5%
Common
ValueCountFrequency (%)
25651
84.4%
(1246
 
4.1%
)1115
 
3.7%
-864
 
2.8%
/722
 
2.4%
*131
 
0.4%
.99
 
0.3%
285
 
0.3%
=58
 
0.2%
158
 
0.2%
Other values (18)348
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII401057
> 99.9%
None199
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A70573
17.6%
I42535
10.6%
E37729
9.4%
R31109
 
7.8%
25651
 
6.4%
N25059
 
6.2%
L24333
 
6.1%
O22522
 
5.6%
S18852
 
4.7%
M14743
 
3.7%
Other values (43)87951
21.9%
None
ValueCountFrequency (%)
Ç127
63.8%
Ã35
 
17.6%
Á11
 
5.5%
Í7
 
3.5%
É6
 
3.0%
Â3
 
1.5%
À3
 
1.5%
Ú3
 
1.5%
Ô2
 
1.0%
´1
 
0.5%

PERSONAL_REFERENCE_#2
Categorical

HIGH CARDINALITY
MISSING

Distinct13672
Distinct (%)38.2%
Missing4190
Missing (%)10.5%
Memory size312.6 KiB
MARIA
 
550
MARCIA
 
274
SONIA
 
266
FATIMA
 
259
SANDRA
 
231
Other values (13667)
34230 

Length

Max length32
Median length23
Mean length8.937084613
Min length1

Characters and Unicode

Total characters320037
Distinct characters64
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11673 ?
Unique (%)32.6%

Sample

1st rowFELIPE
2nd rowVALERIA ALEXANDRA TRAJANO
3rd rowSANDRO L P MARTINS
4th rowANA
5th rowEDU (AVO)

Common Values

ValueCountFrequency (%)
MARIA550
 
1.4%
MARCIA274
 
0.7%
SONIA266
 
0.7%
FATIMA259
 
0.6%
SANDRA231
 
0.6%
LUCIA228
 
0.6%
ANA223
 
0.6%
VERA210
 
0.5%
CLAUDIA193
 
0.5%
REGINA189
 
0.5%
Other values (13662)33187
83.0%
(Missing)4190
 
10.5%

Length

2022-06-22T16:51:46.780262image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
maria2369
 
4.5%
1139
 
2.2%
ana724
 
1.4%
de569
 
1.1%
jose559
 
1.1%
lucia550
 
1.1%
silva513
 
1.0%
da511
 
1.0%
fatima403
 
0.8%
marcia392
 
0.8%
Other values (7840)44337
85.2%

Most occurring characters

ValueCountFrequency (%)
A57006
17.8%
I34826
10.9%
E30120
9.4%
R24664
 
7.7%
N20985
 
6.6%
L20063
 
6.3%
O17898
 
5.6%
16957
 
5.3%
S14186
 
4.4%
M11750
 
3.7%
Other values (54)71582
22.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter298880
93.4%
Space Separator16957
 
5.3%
Open Punctuation1164
 
0.4%
Close Punctuation1088
 
0.3%
Dash Punctuation818
 
0.3%
Other Punctuation676
 
0.2%
Decimal Number396
 
0.1%
Math Symbol57
 
< 0.1%
Modifier Symbol1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A57006
19.1%
I34826
11.7%
E30120
10.1%
R24664
8.3%
N20985
 
7.0%
L20063
 
6.7%
O17898
 
6.0%
S14186
 
4.7%
M11750
 
3.9%
D11603
 
3.9%
Other values (27)55779
18.7%
Decimal Number
ValueCountFrequency (%)
367
16.9%
258
14.6%
642
10.6%
141
10.4%
736
9.1%
036
9.1%
434
8.6%
832
8.1%
925
 
6.3%
525
 
6.3%
Other Punctuation
ValueCountFrequency (%)
/509
75.3%
.87
 
12.9%
*60
 
8.9%
\8
 
1.2%
'6
 
0.9%
,3
 
0.4%
;2
 
0.3%
&1
 
0.1%
Open Punctuation
ValueCountFrequency (%)
(1162
99.8%
[2
 
0.2%
Close Punctuation
ValueCountFrequency (%)
)1087
99.9%
]1
 
0.1%
Math Symbol
ValueCountFrequency (%)
=53
93.0%
+4
 
7.0%
Space Separator
ValueCountFrequency (%)
16957
100.0%
Dash Punctuation
ValueCountFrequency (%)
-818
100.0%
Modifier Symbol
ValueCountFrequency (%)
`1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin298880
93.4%
Common21157
 
6.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
A57006
19.1%
I34826
11.7%
E30120
10.1%
R24664
8.3%
N20985
 
7.0%
L20063
 
6.7%
O17898
 
6.0%
S14186
 
4.7%
M11750
 
3.9%
D11603
 
3.9%
Other values (27)55779
18.7%
Common
ValueCountFrequency (%)
16957
80.1%
(1162
 
5.5%
)1087
 
5.1%
-818
 
3.9%
/509
 
2.4%
.87
 
0.4%
367
 
0.3%
*60
 
0.3%
258
 
0.3%
=53
 
0.3%
Other values (17)299
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII319838
99.9%
None199
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A57006
17.8%
I34826
10.9%
E30120
9.4%
R24664
 
7.7%
N20985
 
6.6%
L20063
 
6.3%
O17898
 
5.6%
16957
 
5.3%
S14186
 
4.4%
M11750
 
3.7%
Other values (43)71383
22.3%
None
ValueCountFrequency (%)
Ç125
62.8%
Ã27
 
13.6%
É19
 
9.5%
Á10
 
5.0%
Ú7
 
3.5%
Í6
 
3.0%
À1
 
0.5%
Ê1
 
0.5%
Ì1
 
0.5%
Ô1
 
0.5%

FLAG_MOBILE_PHONE
Boolean

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
False
40000 
ValueCountFrequency (%)
False40000
100.0%
2022-06-22T16:51:47.393948image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

FLAG_CONTACT_PHONE
Boolean

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
False
40000 
ValueCountFrequency (%)
False40000
100.0%
2022-06-22T16:51:47.874144image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

PERSONAL_NET_INCOME
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct2315
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9752.711101
Minimum0
Maximum38529098
Zeros956
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size312.6 KiB
2022-06-22T16:51:48.528168image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile180
Q1270
median400
Q3738
95-th percentile1739
Maximum38529098
Range38529098
Interquartile range (IQR)468

Descriptive statistics

Standard deviation485633.5056
Coefficient of variation (CV)49.79471867
Kurtosis3211.137561
Mean9752.711101
Median Absolute Deviation (MAD)177
Skewness55.52751098
Sum390108444.1
Variance2.358399017 × 1011
MonotonicityNot monotonic
2022-06-22T16:51:49.260514image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3002007
 
5.0%
5001609
 
4.0%
4001589
 
4.0%
1801457
 
3.6%
6001227
 
3.1%
3501024
 
2.6%
200987
 
2.5%
800966
 
2.4%
0956
 
2.4%
220949
 
2.4%
Other values (2305)27229
68.1%
ValueCountFrequency (%)
0956
2.4%
131
 
0.1%
33
 
< 0.1%
61
 
< 0.1%
1018
 
< 0.1%
201
 
< 0.1%
211
 
< 0.1%
222
 
< 0.1%
252
 
< 0.1%
331
 
< 0.1%
ValueCountFrequency (%)
385290981
< 0.1%
286600001
< 0.1%
267695271
< 0.1%
259000001
< 0.1%
258022361
< 0.1%
255703821
< 0.1%
251219581
< 0.1%
250887241
< 0.1%
250752281
< 0.1%
246736011
< 0.1%

COD_APPLICATION_BOOTH
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.6 KiB
0
40000 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters40000
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
040000
100.0%

Length

2022-06-22T16:51:50.683056image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-22T16:51:51.730385image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
040000
100.0%

Most occurring characters

ValueCountFrequency (%)
040000
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number40000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
040000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common40000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
040000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII40000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
040000
100.0%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.6 KiB
0
34779 
1
4488 
2
 
730
3
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters40000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
034779
86.9%
14488
 
11.2%
2730
 
1.8%
33
 
< 0.1%

Length

2022-06-22T16:51:52.606283image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-22T16:51:53.633248image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
034779
86.9%
14488
 
11.2%
2730
 
1.8%
33
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
034779
86.9%
14488
 
11.2%
2730
 
1.8%
33
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number40000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
034779
86.9%
14488
 
11.2%
2730
 
1.8%
33
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common40000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
034779
86.9%
14488
 
11.2%
2730
 
1.8%
33
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII40000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
034779
86.9%
14488
 
11.2%
2730
 
1.8%
33
 
< 0.1%

FLAG_CARD_INSURANCE_OPTION
Boolean

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
False
40000 
ValueCountFrequency (%)
False40000
100.0%
2022-06-22T16:51:54.645908image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

TARGET_LABEL_BAD=1
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.6 KiB
0
32100 
1
7900 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters40000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
032100
80.2%
17900
 
19.8%

Length

2022-06-22T16:51:55.508941image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-22T16:51:56.575284image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
032100
80.2%
17900
 
19.8%

Most occurring characters

ValueCountFrequency (%)
032100
80.2%
17900
 
19.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number40000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
032100
80.2%
17900
 
19.8%

Most occurring scripts

ValueCountFrequency (%)
Common40000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
032100
80.2%
17900
 
19.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII40000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
032100
80.2%
17900
 
19.8%

Interactions

2022-06-22T16:50:43.509882image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:49:07.290773image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:49:20.356269image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:49:32.633631image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:49:45.134615image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:49:58.658560image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:50:10.675048image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:50:17.960198image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:50:24.122420image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:50:32.992472image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:50:44.519907image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:49:09.102977image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:49:21.389432image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:49:33.592348image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:49:46.236893image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:49:59.870852image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:50:11.451566image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:50:18.575598image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:50:24.715462image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:50:34.037338image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:50:45.514907image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:49:10.282563image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:49:22.481176image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:49:34.547735image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:49:47.393631image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:50:01.054374image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:50:12.406963image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:50:19.183931image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:50:25.267135image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:50:35.092554image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:50:46.567903image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:49:11.538964image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:49:23.755022image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:49:35.504354image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:49:48.716698image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:50:02.292739image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:50:13.189866image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:50:19.795938image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:50:25.873974image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:50:36.205777image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:50:47.658882image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:49:12.741786image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:49:24.983146image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:49:36.818792image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:49:50.057491image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:50:03.598509image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:50:13.833235image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:50:20.446355image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:50:26.787979image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:50:37.314451image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:50:48.687903image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:49:14.270476image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:49:26.148017image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:49:38.247897image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:49:51.455164image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:50:04.827425image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:50:14.843453image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:50:21.035064image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:50:27.772345image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:50:38.360379image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:50:49.740901image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:49:15.594036image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:49:27.365290image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:49:39.296715image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:49:52.980701image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:50:06.045479image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:50:15.448036image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:50:21.649310image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:50:28.263927image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:50:39.419378image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:50:50.809891image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:49:16.865163image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:49:28.658044image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:49:40.584674image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:49:54.294947image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:50:07.327434image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:50:16.081979image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:50:22.243827image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:50:29.165933image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:50:40.437915image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:50:51.863907image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:49:18.115347image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:49:29.935263image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:49:41.875933image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:49:55.595495image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:50:08.561908image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:50:16.667862image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:50:22.851912image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:50:30.302156image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:50:41.443882image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:50:52.931904image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:49:19.204167image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:49:31.436139image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:49:43.281745image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:49:57.229154image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:50:09.635255image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:50:17.314389image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:50:23.477916image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:50:31.365358image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-22T16:50:42.414889image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-06-22T16:51:57.585754image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-06-22T16:52:00.144492image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-06-22T16:52:02.328707image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-06-22T16:52:05.397087image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-06-22T16:52:07.846804image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-06-22T16:50:55.133879image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-06-22T16:51:04.273212image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-06-22T16:51:08.280342image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-06-22T16:51:09.632340image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

ID_CLIENTID_SHOPSEXMARITAL_STATUSAGEQUANT_DEPENDANTSEDUCATIONFLAG_RESIDENCIAL_PHONEAREA_CODE_RESIDENCIAL_PHONEPAYMENT_DAYSHOP_RANKRESIDENCE_TYPEMONTHS_IN_RESIDENCEFLAG_MOTHERS_NAMEFLAG_FATHERS_NAMEFLAG_RESIDENCE_TOWN=WORKING_TOWNFLAG_RESIDENCE_STATE=WORKING_STATEMONTHS_IN_THE_JOBPROFESSION_CODEMATE_INCOMEFLAG_RESIDENCIAL_ADDRESS=POSTAL_ADDRESSFLAG_OTHER_CARDQUANT_BANKING_ACCOUNTSPERSONAL_REFERENCE_#1PERSONAL_REFERENCE_#2FLAG_MOBILE_PHONEFLAG_CONTACT_PHONEPERSONAL_NET_INCOMECOD_APPLICATION_BOOTHQUANT_ADDITIONAL_CARDS_IN_THE_APPLICATIONFLAG_CARD_INSURANCE_OPTIONTARGET_LABEL_BAD=1
0215FS180NaNY31200P216YYYY128530.0YN0SARAFELIPENN300.000N0
1412FC470NaNN31250P180YYNY24350.0YN0JACIVALERIA ALEXANDRA TRAJANONN304.000N0
2516FS280NaNY31250O12YYYY12240.0YN0MARCIA CRISTINA ZANELLASANDRO L P MARTINSNN250.000N0
3624MS260NaNN31280P180YYNY09990.0YN0MARCIOANANN800.000N0
4755FS220NaNY31120A0YYYY489990.0YN0FABIO (NOIVO)EDU (AVO)NN410.000N0
586FC210NaNY23280A24YYYY1240800.0YN0OLIONA MARIA CAMPOSELIZETE CAMPS COELHONN248.000N0
693FS270NaNY31200A0YYYY09500.0YN0SUELIREGINANN1000.000N1
71023FC570NaNY31120P24YYNY96130.0YN0MARIA DE LOURDESZILDANN856.000N0
81125FS530NaNY31180P60YYNY24130.0YN0ANAMARIA MONICANN738.001N1
91212FC320NaNY31120P24YYNY01650.0YN0ESTELLA OSVALDO CRUZANA MARIANN700.000N0

Last rows

ID_CLIENTID_SHOPSEXMARITAL_STATUSAGEQUANT_DEPENDANTSEDUCATIONFLAG_RESIDENCIAL_PHONEAREA_CODE_RESIDENCIAL_PHONEPAYMENT_DAYSHOP_RANKRESIDENCE_TYPEMONTHS_IN_RESIDENCEFLAG_MOTHERS_NAMEFLAG_FATHERS_NAMEFLAG_RESIDENCE_TOWN=WORKING_TOWNFLAG_RESIDENCE_STATE=WORKING_STATEMONTHS_IN_THE_JOBPROFESSION_CODEMATE_INCOMEFLAG_RESIDENCIAL_ADDRESS=POSTAL_ADDRESSFLAG_OTHER_CARDQUANT_BANKING_ACCOUNTSPERSONAL_REFERENCE_#1PERSONAL_REFERENCE_#2FLAG_MOBILE_PHONEFLAG_CONTACT_PHONEPERSONAL_NET_INCOMECOD_APPLICATION_BOOTHQUANT_ADDITIONAL_CARDS_IN_THE_APPLICATIONFLAG_CARD_INSURANCE_OPTIONTARGET_LABEL_BAD=1
399904998919MS280NaNY31180P336YYNY125140.0YN0SHEILA JUSSARA M CASTELANSHIRLEYNN691.000N0
399914999024MC580NaNY50200P180YYNY729990.0YN0SILVANIA / ROSEAROLDONN900.001N0
399924999115MC430NaNY31280P108YYNY1449210.0YN0CECILIAMARISANN3500.001N1
399934999225FS230NaNY31200A180YYYY248010.0YN0FABIANACLAUDIANN362.000N0
399944999318FC380NaNY23183P192YYNY09990.0YN0GEOVANE S. RIBEIROSIDNÉIA MOZERNN0.000N0
39995499941MC290NaNY31120A36YYNY243050.0YN0RUTHNaNNN796.001N1
399964999512FS200NaNY31200P180YYYY127120.0YN0DALVA DE AZEVEDOANANN200.000N0
399974999619MS210NaNY31120P120YYYY122180.0YN0ALBADENILSONNN234.000N0
399984999823FS230NaNY31280P264YYYY129910.0YN0NOVINAGLAUCIANN240.000N1
399995000022MS290NaNY31230P48YYNY36260.0YN0TITO MARTINSNaNNN341.000N0